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Student projects available

Below follows a list of project descriptions for students. Some of the projects are finished, some are in progress, and some are still available to students that want to do a UG4, MSc or a PhD projects.

The project would suit a student with strong statistical skills and a background in
Neuroinformatics, Neuroscience, or Psychology but also potentially a student from a pure
Statistical, Mathematical, or Engineering background and an interest in brain ageing and
pathology. It requires understanding of statistical analyses and summaries (e.g. hypotheses
testing, means, and percentiles), clinical and brain image data, the sensitivities and
management of these data; and the ability to work as part of an interdisciplinary group of
researchers. There will be additional mentorship from Prof Joanna Wardlaw (expertise in
neuroimaging).

Multimodal Image data banks, of normal [3] and pathological subjects, are of great utility for improving collaboration and performing research with greater statistical power. The acquisition of images is expensive and time consuming; therefore it is important to reuse them. We are currently developing human brain image data banks, one of which is likely to be the largest bank of normal aging brains in the world.

Outcome after severe ischemic stroke may improve with thrombolysis. Some studies have shown that parametric perfusion maps and other information might be useful in selecting patients for this potentially hazardous treatment. Traditionally, perfusion source images are deconvolved in order to create these parametric maps; such as cerebral blood flow and volume [1].

With the increasing resolution of MR and CT scans, it has become feasible to reconstruct detailed 3D images of faces.

Usually face de-identification in medical imaging is done after the reconstruction, i.e. in 3D (see references). Different techniques are used to this end including brain extraction, removal of facial features and deformation of the face surface.

We are building a data-intensive machine as a research platform to explore data-intensive computational strategies. We are interested in computations over large bodies of data, where the data-handling is a dominant issue. Computational challenges with these properties are getting ever more prevalent as the cost of digital sensors and computational/societal data sources become ever cheaper, ever more powerful and more ubiquitous. The use of algorithms over such data are of growing importance in medicine, planning, engineering, policy and science.